Different ML-based strategies for customer churn prediction in banking sector
- 4 July 2024
- journal article
- research article
- Published by Springer Nature in Journal of Data, Information and Management
- Vol. 6 (3) , 217-234
Abstract
No abstract availableKeywords
This publication has 29 references indexed in Scilit:
- An attention-based deep learning method for solving the cold-start and sparsity issues of recommender systemsKnowledge-Based Systems, 2022
- Propension to customer churn in a financial institution: a machine learning approachNeural Computing and Applications, 2022
- Customer Churn Prediction in Influencer Commerce: An Application of Decision TreesProcedia Computer Science, 2022
- CFMT: a collaborative filtering approach based on the nonnegative matrix factorization technique and trust relationshipsJournal of Ambient Intelligence and Humanized Computing, 2021
- Giant fight: Customer churn prediction in traditional broadcast industryJournal of Business Research, 2021
- Machine Learning for Customer Churn Prediction in Retail Banking2020
- Churning of Bank Customers Using Supervised Learning2020
- Semi-random partitioning of data into training and test sets in granular computing contextGranular Computing, 2017
- Prediction of Customer Attrition of Commercial Banks based on SVM ModelProcedia Computer Science, 2014
- Credit card churn forecasting by logistic regression and decision treeExpert Systems with Applications, 2011